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Abstract Multisystem Inflammatory Syndrome in Children (MIS-C) is a rare manifestation of Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2) infection that can result in increased morbidity and mortality. Mounting evidence describes sex disparities in the clinical outcomes of coronavirus disease 2019 (COVID-19). However, there is a lack of information on sex-specific differences in immune responses in MIS-C. This study is an observational and cross-sectional study and we wanted to examine immune parameters such as cytokines, chemokines, acute phase proteins (APPs), growth factors, microbial translocation markers (MTMs), complement components and matrix metalloproteinases (MMPs) in MIS-C children, based on sex. Male children were associated with heightened levels of pro-inflammatory cytokines—IFNγ, IL-2, TNFα, IL-1α, IL-1β, IL-6, IL-12, G-CSF and GM-CSF, chemokines-CCL2, CCL11, CXCL1, CXCL8 and CXCL10, acute phase proteins-α-2M, CRP, growth factors VEGF and TGFα, microbial translocation markers- iFABP, LBP, EndoCAb, complement components—C1q, MBL and C3 and matrix metalloproteinases MMP-8 and MMP-9 compared to female children with MIS-C. These results indicate that the heightened immune response in males is a characteristic feature of MIS-C. These findings might explain the differential disease pathogenesis in males compared to females with MIS-C and facilitate a deeper understanding of this disease.

IntroductionMultisystem Inflammatory Syndrome in children (MIS-C) is a serious inflammatory sequela of SARS-CoV2 infection. The pathogenesis of MIS-C is vague and matrix metalloproteinases (MMPs) may have an important role. Matrix metalloproteinases (MMPs) are known drivers of lung pathology in many diseases.MethodsTo elucidate the role of MMPs in pathogenesis of pediatric COVID-19, we examined their plasma levels in MIS-C and acute COVID-19 children and compared them to convalescent COVID-19 and children with other common tropical diseases (with overlapping clinical manifestations).ResultsChildren with MIS-C had elevated levels of MMPs (P < 0.005 statistically significant) in comparison to acute COVID-19, other tropical diseases (Dengue fever, typhoid fever, and scrub typhus fever) and convalescent COVID-19 children. PCA and ROC analysis (sensitivity 84–100% and specificity 80–100%) showed that MMP-8, 12, 13 could help distinguish MIS-C from acute COVID-19 and other tropical diseases with high sensitivity and specificity. Among MIS-C children, elevated levels of MMPs were seen in children requiring intensive care unit admission as compared to children not needing intensive care. Similar findings were noted when children with severe/moderate COVID-19 were compared to children with mild COVID-19. Finally, MMP levels exhibited significant correlation with laboratory parameters, including lymphocyte counts, CRP, D-dimer, Ferritin and Sodium levels.DiscussionOur findings suggest that MMPs play a pivotal role in the pathogenesis of MIS-C and COVID-19 in children and may help distinguish MIS-C from other conditions with overlapping clinical presentation.

The clinical presentation of MIS-C overlaps with other infectious/non-infectious diseases such as acute COVID-19, Kawasaki disease, acute dengue, enteric fever, and systemic lupus erythematosus. We examined the ex-vivo cellular parameters with the aim of distinguishing MIS-C from other syndromes with overlapping clinical presentations. MIS-C children differed from children with non-MIS-C conditions by having increased numbers of naïve CD8+ T cells, naïve, immature and atypical memory B cells and diminished numbers of transitional memory, stem cell memory, central and effector memory CD4+ and CD8+ T cells, classical, activated memory B and plasma cells and monocyte (intermediate and non-classical) and dendritic cell (plasmacytoid and myeloid) subsets. All of the above alterations were significantly reversed at 6-9 months post-recovery in MIS-C. Thus, MIS-C is characterized by a distinct cellular signature that distinguishes it from other syndromes with overlapping clinical presentations. Trial Registration: ClinicalTrials.gov clinicaltrial.gov. No: NCT04844242.

Detection and clustering of commercial advertisements plays an important role in multimedia indexing also in the creation of personalized user content. In existing methodologies, the mining techniques were text, image, audio retrieval based on knowledge based environment and commercial video retrieval based on rule-based algorithms, logo-based algorithms, recognition based methods. The quality video with enhanced accuracy has been detected using the automated commercial and general program for video detection technique. The clustering technique implements the clustering process for the entire video to the frames and the required main frames are depended on the total amount of frames. The key frames are extracted from the video sequences and the duplicate key frames are eliminated from the video sequences. The video compression feature has been optimized using hybrid end-to-end compression technique to extract the features and reconstruct the video frames. The main encoding algorithm performs the optimization to produce the encoded frames and it is further compressed using the additional encoding algorithm. The performance results show that the proposed technique has the improved performances in terms of MSE, PSNR, SSIM, compression ratio and the computation time which is compared with the related techniques. [ABSTRACT FROM AUTHOR]

Advancement of the public transport system is important to modern society for reliable performance. Intelligent public transport system can utilize the time very effectively to give better performance to the society. Fast advancement in equipment, programming, and correspondence innovations has encouraged the rise of Internetassociated devices that give perceptions and information gathering from modern reality. By interfacing, an internetenabled device with the public transport system leads to the intelligent public transport system. This paper proposed intelligent public transport with IoT enabled system to give an accurate prediction to the arrival time of the bus to the particular bus stops. Here, Artificial Neural Network (ANN) is used as a prediction algorithm and ANN is trained with different traffic parameters and environmental conditions. Parameters which are considered in the proposed system includes Distance(D), Waiting Time at Stops (WTS), Red signal Duration at Traffic Signal (RSD), Traffic Density (TD), Turning Density (TRD), Rush hours (RH), Weekends (WE), Weather conditions (WC), Number of passengers in the bus (NP), Public Holiday (PH), Road Type (RT). These parameters and the measured real-time arrival time of the bus in different stops for 10 days is used for training the ANN. This trained ANN is implemented on the server-side. In this paper, the performance of the proposed IOT-ANN-SPTS system is compared with the existing previous related works. From the performance analysis, it is shown that the proposed system produced less error (MAE=134.582, RMSE=197.738, MAPE=28.87) when compared to the other methods. [ABSTRACT FROM AUTHOR]